1. Technical Field
The present invention relates to visualization and computer aided diagnosis and detection of pulmonary embolism, and more particularly, to a system and method for tree-model visualization for pulmonary embolism detection.
2. Discussion of the Related Art
A pulmonary embolism (PE) occurs when a piece of a blood clot from a deep vein thrombosis (DVT) breaks off and travels to an artery in a lung where it blocks the artery, damages the lung and puts a strain on the heart. This short-term complication is potentially life threatening and occurs in about ten percent of patients with acute DVT events. It may be even more common than generally realized because the majority of embolisms occur without symptoms.
Although PE is one of the most common causes of unexpected death in the United States, it may also be one of the most preventable. Prompt treatment with anticoagulants is essential to prevent loss of life. However, such treatment carries risks, thus making correct diagnosis critical. As a result, computed tomography angiography (CTA) is gaining increasing acceptance as a method of diagnosis by offering sensitivity and specificity comparable or superior to alternative methods such as pulmonary angiography and ventilation-perfusion scans.
Images acquired from 16-slice computed tomography (CT) scanners used during CTA provide very high-resolution data allowing for enhanced detection of emboli located in sub-segmental arteries. Analysis of the high-resolution data via two-dimensional (2D) slices involves tracking individual vessels and examining their contents. This analysis, however, can be time consuming, especially for peripheral arteries. For example, a medical practitioner such as a radiologist must navigate through individual 2D slices while at the same time remembering the locations of the vessels being tracked. However, because the radiologist can only track a limited number of vessels at one time, the entire tracking process must be repeated.
Current research in the area of automated analysis of PE within contrast-enhanced CT images concerns either the direct detection of clots within the arteries by means of computer aided detection (CAD) or the indirect inference of clot location by visualization of the vessels or perfusion defects in affected lung areas. When detecting clots within the arteries using CAD, a good segmentation of the arteries is generally required to detect precise locations of PE. Once the PE candidates are automatically identified, they are presented to a radiologist for verification. Since PE CAD candidates are automatically identified, some PE locations may be missed and false positives may occur. In addition, the radiologist is typically given no information as to why a particular PE location was chosen or not.
In another method for PE visualization, the mean density of local areas of the lungs is computed and rendered to directly visualize perfusion defects. Lung areas showing lower than average density are typically suggestive of an upstream clot; however, other conditions such as emphysema may also result in below average intensity. This method involves similar navigational requirements to that of 2D slice viewing, namely, scrolling through 2D slice sections and remembering the locations of certain patterns.
In yet another PE visualization method, a shaded three-dimensional (3D) vessel tree uses internal density regions of the vessels to color the tree surface. This method simplifies the search for peripheral PE because vessel tracking is no longer necessary. However, the visualized tree must be navigated in 3D, and because branches tend to occlude other branches, navigation around the tree requires a significant amount of interaction. In addition, the entire surface of each branch may need to be examined, thus requiring a full rotational view of each branch resulting in a time-consuming process.
Accordingly, there is a need for a technique that reduces or eliminates the need for 3D navigation for viewing all structures of a 3D vessel tree.